code: def validate_provider_availability(provider: str):
      """
      Ensure the ONNX Runtime execution provider `provider` is available, and raise an error if it is not.
  
      Args:
          provider (str): Name of an ONNX Runtime execution provider.
      """
      # Disable on Windows as reported in https://github.com/huggingface/optimum/issues/769.
      # Disable as well for ORT 1.16.0 that has changed changed the way _ld_preload.py is filled: https://github.com/huggingface/optimum/issues/1402.
      if (
          version.parse(ort.__version__) < version.parse("1.16.0")
          and os.name != "nt"
          and provider
          in ["CUDAExecutionProvider", "TensorrtExecutionProvider"]
      ):
          path_cuda_lib = os.path.join(
              ort.__path__[0],
              "capi",
              "libonnxruntime_providers_cuda.so",
          )
          path_trt_lib = os.path.join(
              ort.__path__[0],
              "capi",
              "libonnxruntime_providers_tensorrt.so",
          )
          path_dependecy_loading = os.path.join(
              ort.__path__[0], "capi", "_ld_preload.py"
          )
  
          with open(path_dependecy_loading, "r") as f:
              file_string = f.read()
  
              if (
                  "ORT_CUDA" not in file_string
                  or "ORT_TENSORRT" not in file_string
              ):
                  if os.path.isfile(path_cuda_lib) and os.path.isfile(
                      path_trt_lib
                  ):
                      raise ImportError(
                          f"`onnxruntime-gpu` is installed, but GPU dependencies are not loaded. It is likely there is a conflicting install between `onnxruntime` and `onnxruntime-gpu`. Please install only `onnxruntime-gpu` in order to use {provider}."
                      )
                  elif (
                      os.path.isfile(path_cuda_lib)
                      and is_onnxruntime_training_available()
                  ):
                      if provider == "TensorrtExecutionProvider":
                          raise ImportError(
                              f"Asked to use {provider}, but `onnxruntime-training` package doesn't support {provider}. Please use `CUDAExecutionProvider` instead."
                          )
                  else:
                      raise ImportError(
                          f"Asked to use {provider}, but `onnxruntime-gpu` package was not found. Make sure to install `onnxruntime-gpu` package instead of `onnxruntime`."
                      )
  
              if provider == "CUDAExecutionProvider":
                  if os.environ.get("ORT_CUDA_UNAVAILABLE", "0") == "1":
                      raise ImportError(
                          "`onnxruntime-gpu` package is installed, but CUDA requirements could not be loaded. Make sure to meet the required dependencies: https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html"
                      )
              if provider == "TensorrtExecutionProvider":
                  if (
                      os.environ.get("ORT_TENSORRT_UNAVAILABLE", "0")
                      == "1"
                  ):
                      raise ImportError(
                          "`onnxruntime-gpu` package is installed, but TensorRT requirements could not be loaded. Make sure to meet the required dependencies following https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html and https://hf.co/docs/optimum/onnxruntime/usage_guides/gpu#tensorrtexecutionprovider ."
                      )
  
      available_providers = ort.get_available_providers()
      if provider not in available_providers:
          raise ValueError(
              f"Asked to use {provider} as an ONNX Runtime execution provider, but the available execution providers are {available_providers}."
          )
  
imports: import os
  import re
  from enum import Enum
  from inspect import signature
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from packaging import version
  from transformers.utils import logging
  import onnxruntime
  from ..exporters.onnx import OnnxConfig, OnnxConfigWithLoss
  from ..utils.import_utils import _is_package_available
label_desc: Use image node_id 9 for calling a global function with example usage: validate_provider_availability(provider) without return types